Marmasse and Schmandt “A User-centred Location Model”, Personal and Ubiquitous Computing, 2002, vol. 6, pp 318-321, Springer-Verlag London Limited disclose a system for learning frequented places by noting locations where a vehicle or user has often been stationary for some while. A user is then invited to name such a place, at which time it becomes a candidate for prediction. There is also a training phase, where it appears that the journey models in use are fed with trial journeys already classified by researchers as the route to which they belong. This is a phase which requires much user intervention, including associating trial runs of a route with its particular model, and is not appropriate for an automated system. The training data for each route is used to train a variety of models, such as a Bayes Classifier, Histogram Modelling and a Hidden Markov Model.
US 2002/0161517 A1 by Pioneer discloses a system for predicting a destination using the following: (a) those destinations that a user has entered in the past, (b) a road mapping database, (c) the recent history of the current journey, and (d) a travel information database built by matching past journeys against the road mapping database. It only begins to record the route used to a destination once such a destination has been identified. It therefore generally requires user intervention to identify destinations before it can be of use. There is a suggestion that the system could detect a position where the engine of a vehicle is stopped regularly and record that as a destination point. After that time, if the user selects that point as a destination, then the system could learn a route to it and begin to predict it automatically. However, the user still has to select that point manually as a destination at some stage.
EP0967460 A1 discloses a system for learning a commute route, that is, a single route along which the driver travels most frequently. It requires the user to enter a time window during which the commute is made, or uses a default time setting, and then attempts to deduce the commute route by storing the route most recently used, by storing a composite route whose derivation is undefined, or by counting how many times each road segment from a mapping database is traversed during that time window and using the most frequently traveled road segments as the commute route.